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Record W189215230

Design of CMOS based transimpedance amplifier for integrated optical MEMS applications

2005· article· en· W189215230 on OpenAlexaff
Paresh Rathod, Muthukumaran Packirisamy, Ion Stiharu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCCD and CMOS Imaging Sensors
Canadian institutionsConcordia University
Fundersnot available
KeywordsTransimpedance amplifierPhotodiodeCMOSAmplifierOperational amplifierOptoelectronicsWide dynamic rangeElectronic engineeringMaterials scienceEngineering
DOInot available

Abstract

fetched live from OpenAlex

Novel and simple designs of transimpedance amplifiers (TIA) that can be integrated with optical MEMS devices and optical sensors for conversion of current signal into voltage are proposed using standard CMOS technology. The transimpedance amplifiers are designed with variable gain and dynamic range so that they can be selected depending upon the specific application requirement. Design of different types of photodiodes using the TSMC 0.18µm CMOS technology was implemented using the proposed TIA. It is known that CMOS photodiode sensitivity is limited to light wavelength from 100nm to 1100nm. Even though now days CMOS photodiode are widely used as photo-detectors because of its simple layout and easy integration with other circuitries on the same chip at lower cost. Complete design of linear transimpedance amplifier was carried out using Cadence for conversion of photodiode current in to voltage. Photodiodes are utilized in many applications such as spectroscopy, photography, analytical instrumentation, optical position sensors, beam alignment, surface characterization, laser range finders, optical communications, and medical imaging instruments. Proposed CMOS photodiode and Transimpedance amplifiers are suitable for Biophotonics applications. Motivation of this project is the implementation of integrated BioMEMS device for detection of biological and chemical materials.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.812
Threshold uncertainty score0.310

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.238
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2005
Admission routes1
Has abstractyes

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